pow.SEPPLE.random.DE | R Documentation |
Perform the power calculation using the numeric SEPPLE method based on the piecewise weighted log-rank test when the treatment time-lag effect is present and the lag duration varies heterogeneously from individual to individual or from study to study, within a certain domain and following a specific pattern. The purpose of this function is to evaluate the property of SEPPLE which assumes the lag duration is homogeneous across the individual subject, when applied under the random scenario where the lag duration, in fact, varies heterogeneously.
pow.SEPPLE.random.DE(lambda1, tl, tu, N, HR, tao, A, t.fixed, dist="uniform",
shape1=NULL, shape2=NULL, ap=0.5, alpha=0.05, nsim=10000)
lambda1 |
Baseline hazard |
tl |
Lower bound of delayed duration domain |
tu |
Upper bound of delayed duration domain |
N |
Sample size |
HR |
Post-delay hazard ratio after |
tao |
Total study duration |
A |
Total enrollment duration |
t.fixed |
Fixed duration in SEPPLE |
dist |
One of "uniform", "beta" or "gamma", for the lag distribution |
shape1 |
NULL or a positive parameter value for the |
shape2 |
NULL or a positive parameter value for the |
ap |
Experimental-control allocation ratio. The default is 0.5. |
alpha |
Type I error rate (two-sided). The default is 0.05. |
nsim |
Number of simulations. The default is 10000. |
SEPPLE+ is an acronym for:
Simulation-based Empirical Power calculation method based on generalized Piecewise weighted Log-rank
tEst with random treatment time-lag effect.
See the reference for details of this method.
The power
Zhenzhen Xu <Zhenzhen.Xu@fda.hhs.gov> , Boguang Zhen<Boguang.Zhen@fda.hhs.gov>, Yongsoek Park <yongpark@pitt.edu> and Bin Zhu <bin.zhu@nih.gov>
Xu, Z., Park, Y., Zhen, B. & Zhu, B. (2017). Achieving optimal power of logrank test with random treatment time-lag effect. Biometrika. Under review.
Xu, Z., Zhen, B., Park, Y., & Zhu, B. (2017). Designing therapeutic cancer vaccine trials with delayed treatment effect. Statistics in medicine, 36(4), 592-605.
pow.SEPPLE.plus
, pow.sim.logrk.random.DE
lambda1 <- 0.001982
tl <- 30
tu <- 30*11
N <- 200
HR <- 0.55
tao <- 365*3
A <- 365
t.fixed <- (tl+tu)/2
shape1 <- 5
shape2 <- 5
pow.SEPPLE.random.DE(lambda1, tl, tu, N, HR, tao, A, t.fixed, dist="beta",
shape1=shape1, shape2=shape2, nsim=1000)
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